{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "import random\n",
    "\n",
    "import keras\n",
    "np.random.seed(1337)\n",
    "\n",
    "from keras.preprocessing import sequence\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense\n",
    "from keras.layers.core import Dropout\n",
    "from keras.layers.core import Activation\n",
    "from keras.layers.core import Flatten\n",
    "from keras.layers.convolutional import Conv1D\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "### Parameters for plotting model results ###\n",
    "pd.set_option(\"display.max_colwidth\",100)\n",
    "sns.set(style=\"ticks\", color_codes=True)\n",
    "plt.rcParams['font.weight'] = 'normal'\n",
    "plt.rcParams['axes.labelweight'] = 'normal'\n",
    "plt.rcParams['axes.labelpad'] = 5\n",
    "plt.rcParams['axes.linewidth']= 2\n",
    "plt.rcParams['xtick.labelsize']= 14\n",
    "plt.rcParams['ytick.labelsize']= 14\n",
    "plt.rcParams['xtick.major.size'] = 6\n",
    "plt.rcParams['ytick.major.size'] = 6\n",
    "plt.rcParams['xtick.minor.size'] = 3\n",
    "plt.rcParams['ytick.minor.size'] = 3\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "plt.rcParams['ytick.minor.width'] = 1\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "plt.rcParams['xtick.color'] = 'black'\n",
    "plt.rcParams['ytick.color'] = 'black'\n",
    "plt.rcParams['axes.labelcolor'] = 'black'\n",
    "plt.rcParams['axes.edgecolor'] = 'black'\n",
    "\n",
    "\n",
    "def train_model(x, y, border_mode='same', inp_len=50, nodes=40, layers=3, filter_len=8, nbr_filters=120,\n",
    "                dropout1=0, dropout2=0, dropout3=0, nb_epoch=3):\n",
    "    ''' Build model archicture and fit.'''\n",
    "    model = Sequential()\n",
    "    if layers >= 1:\n",
    "        model.add(Conv1D(activation=\"relu\", input_shape=(inp_len, 4), padding=border_mode, filters=nbr_filters, kernel_size=filter_len))\n",
    "    if layers >= 2:\n",
    "        model.add(Conv1D(activation=\"relu\", input_shape=(inp_len, 1), padding=border_mode, filters=nbr_filters, kernel_size=filter_len))\n",
    "        model.add(Dropout(dropout1))\n",
    "    if layers >= 3:\n",
    "        model.add(Conv1D(activation=\"relu\", input_shape=(inp_len, 1), padding=border_mode, filters=nbr_filters, kernel_size=filter_len))\n",
    "        model.add(Dropout(dropout2))\n",
    "    model.add(Flatten())\n",
    "\n",
    "    model.add(Dense(nodes))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Dropout(dropout3))\n",
    "    \n",
    "    model.add(Dense(1))\n",
    "    model.add(Activation('linear'))\n",
    "\n",
    "    #compile the model\n",
    "    adam = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)\n",
    "    model.compile(loss='mean_squared_error', optimizer=adam)\n",
    "\n",
    "    model.fit(x, y, batch_size=128, epochs=nb_epoch, verbose=1)\n",
    "    return model\n",
    "\n",
    "\n",
    "def test_data(df, model, test_seq, obs_col, output_col='pred'):\n",
    "    '''Predict mean ribosome load using model and test set UTRs'''\n",
    "    \n",
    "    # Scale the test set mean ribosome load\n",
    "    scaler = preprocessing.StandardScaler()\n",
    "    scaler.fit(df[obs_col].reshape(-1,1))\n",
    "    \n",
    "    # Make predictions\n",
    "    predictions = model.predict(test_seq).reshape(-1)\n",
    "    \n",
    "    # Inverse scaled predicted mean ribosome load and return in a column labeled 'pred'\n",
    "    df.loc[:,output_col] = scaler.inverse_transform(predictions)\n",
    "    return df\n",
    "\n",
    "\n",
    "def one_hot_encode(df, col='utr', seq_len=50):\n",
    "    # Dictionary returning one-hot encoding of nucleotides. \n",
    "    nuc_d = {'a':[1,0,0,0],'c':[0,1,0,0],'g':[0,0,1,0],'t':[0,0,0,1], 'n':[0,0,0,0]}\n",
    "    \n",
    "    # Creat empty matrix.\n",
    "    vectors=np.empty([len(df),seq_len,4])\n",
    "    \n",
    "    # Iterate through UTRs and one-hot encode\n",
    "    for i,seq in enumerate(df[col].str[:seq_len]): \n",
    "        seq = seq.lower()\n",
    "        a = np.array([nuc_d[x] for x in seq])\n",
    "        vectors[i] = a\n",
    "    return vectors\n",
    "\n",
    "\n",
    "def r2(x,y):\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)\n",
    "    return r_value**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data, make a train and test set based on total reads per UTR\n",
    "The test set contains UTRs with the highest overall sequencing reads with the idea that increased reads will more accurately reflect the true ribosome load of a given 5'UTR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.py:288: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.py:465: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_pickle('../data/egfp_unmod_1.pkl')\n",
    "df.sort_values('total_reads', inplace=True, ascending=False)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df = df.iloc[:280000]\n",
    "\n",
    "# The training set has 260k UTRs and the test set has 20k UTRs.\n",
    "e_test = df.iloc[:20000]\n",
    "e_train = df.iloc[20000:]\n",
    "\n",
    "# One-hot encode both training and test UTRs\n",
    "seq_e_train = one_hot_encode(e_train,seq_len=50)\n",
    "seq_e_test = one_hot_encode(e_test, seq_len=50)\n",
    "\n",
    "# Scale the training mean ribosome load values\n",
    "e_train.loc[:,'scaled_rl'] = preprocessing.StandardScaler().fit_transform(e_train.loc[:,'rl'].reshape(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train model\n",
    "Using the hyperparameter-optimised values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "260000/260000 [==============================] - 292s 1ms/step - loss: 0.2528\n",
      "Epoch 2/3\n",
      " 57088/260000 [=====>........................] - ETA: 3:47 - loss: 0.1680"
     ]
    }
   ],
   "source": [
    "model = train_model(seq_e_train, e_train['scaled_rl'], nb_epoch=3,border_mode='same',\n",
    "                   inp_len=50, nodes=40, layers=3, nbr_filters=120, filter_len=8, dropout1=0,\n",
    "                   dropout2=0,dropout3=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate model. Return predicted mean ribosome load as a dataframe column labeled 'pred'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r-squared =  0.9338504471975527\n"
     ]
    }
   ],
   "source": [
    "e_test = test_data(df=e_test, model=model, obs_col='rl',test_seq=seq_e_test)\n",
    "r = r2(e_test['rl'], e_test['pred'])\n",
    "print 'r-squared = ', r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('./saved_models/my_special_model.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting test results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.load_model('./saved_models/main_MRL_model.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r-squared =  0.9338504471975527\n"
     ]
    }
   ],
   "source": [
    "e_test = test_data(df=e_test, model=model, obs_col='rl',test_seq=seq_e_test)\n",
    "r = r2(e_test['rl'], e_test['pred'])\n",
    "print 'r-squared = ', r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "atg = e_test[e_test['utr'].apply(lambda x: 'ATG' in x)]\n",
    "n_atg = e_test[e_test['utr'].apply(lambda x: 'ATG' not in x)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/matplotlib/font_manager.py:1320: UserWarning: findfont: Font family [u'sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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AR51GtvB31zVHYYZxOhuEedIvbXGnhTLtbkRrz370zCyxH6DjmGIYo/KXEDfq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\ny0gsEYOtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f605e2c74d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c1 = (0.3, 0.45, 0.69)\n",
    "c2 = 'r'\n",
    "g = sns.JointGrid(x='rl', y=\"pred\", data=atg, space=0, xlim=(1,10), ylim=(0,10), ratio=6, size=7)\n",
    "g.plot_joint(plt.scatter,s=20, color=c1, linewidth=0.2, alpha='0.1', edgecolor='white')\n",
    "f = g.fig\n",
    "ax = f.gca()\n",
    "ax.set_yticks(np.arange(0,9.01, 1));\n",
    "ax.set_yticklabels(range(10),size=20);\n",
    "ax.set_xticks(np.arange(1,10.01, 1));\n",
    "ax.set_xticklabels(range(1,11),size=20);\n",
    "ax.set_ylim(0,9)\n",
    "ax.set_xlim(1,10)\n",
    "g.plot_marginals(sns.kdeplot,shade=c1, **{'linewidth':2, 'color':c1})\n",
    "g.set_axis_labels('Observed MRL', 'Predicted MRL', **{'size':22});\n",
    "\n",
    "g.x = n_atg['rl'].values\n",
    "g.y = n_atg['pred'].values\n",
    "g.plot_joint(plt.scatter, s=20, linewidth=0.2, alpha='0.2', color=c2, edgecolor='white')\n",
    "g.plot_marginals(sns.kdeplot, shade=c2, **{'linewidth':2, 'color':c2})\n",
    "f = g.fig"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
